Feature Selection on Classification of Medical Datasets based on Particle Swarm Optimization
|
10.5120/18197-9118 |
Hany M Harb and Abeer S Desuky. Article: Feature Selection on Classification of Medical Datasets based on Particle Swarm Optimization. International Journal of Computer Applications 104(5):14-17, October 2014. Full text available. BibTeX
@article{key:article,
author = {Hany M. Harb and Abeer S. Desuky},
title = {Article: Feature Selection on Classification of Medical Datasets based on Particle Swarm Optimization},
journal = {International Journal of Computer Applications},
year = {2014},
volume = {104},
number = {5},
pages = {14-17},
month = {October},
note = {Full text available}
}
Abstract
Classification analysis is widely adopted for healthcare applications to support medical diagnostic decisions, improving quality of patient care, etc. A subset dataset of the extensive amounts of data stored in medical databases is selected for training. If the training dataset contains irrelevant features, classification analysis may produce less accurate and less understandable results. Feature subset selection is one of data preprocessing step, which is of immense importance in the field of data mining. This paper proposes the filter and wrapper approaches with Particle Swarm Optimization (PSO) as a feature selection methods for medical data. The performance of the proposed methods is compared with another feature selection algorithm based on Genetic approach. The two algorithms are applied to three medical data sets The results show that the feature subset recognized by the proposed PSO when given as input to five classifiers, namely decision tree, Naïve Bayes, Bayesian, Radial basis function and k-nearest neighbor classifiers showed enhanced classification accuracy over all given types of classification methods.
References
- Adam Woznica,Phong Nguyen, Alexandros Kalousis, "Model mining for robust feature selection", KDD '12 Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM New York, NY, USA, PP 913-921, 2012.
- Asha Gowda Karegowda, M. A. Jayaram, A. S . Manjunath, "Feature Subset Selection using Cascaded GA & CFS: A Filter Approach in Supervised Learning", International Journal of Computer Applications (0975 – 8887), Vol. 23– No. 2, June 2011.
- MIT Lincoln Laboratory: http://www. ll. mit. edu/IST/ idaval/.
- Huan Liu, Hiroshi Motoda, Rudy Setiono, Zheng Zhao, "Feature Selection: An Ever Evolving Frontier in Data Mining", JMLR: Workshop and Conference Proceedings Volume 10: 4-13, The Fourth International Workshop on Feature Selection in Data Mining, Hyderabad, India, June 21st, 2010.
- Ian H. Witten and Eibe Frank, "Data Mining: Practical Machine Learning Tools and Techniques", Second Edition, Morgan Kaufmann Publishers, Elsevier Inc. 2005.
- Hany M. Harb, Afaf A. Zaghrot, Mohamed A. Gomaa and Abeer S. Desuky, "Selecting Optimal Subset of Features for Intrusion Detection Systems", Advances in Computational Sciences and echnology, Research India Publications, Volume 4 Number 2, pp. 179-192, 2011.
- XUE, Bing; ZHANG, Mengjie; BROWNE, Will N. , "Multi-objective particle swarm optimisation (PSO) for feature selection", In: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference. ACM, pp. 81-88, 2012.
- LIU, Yuanning, et al. An improved particle swarm optimization for feature selection. Journal of Bionic Engineering, 8. 2: 191-200, 2011. ?
- Hall, Mark A and Smith, Lloyd A, " Feature subset selection: a correlation based filter approach", Springer, 1997.